metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, studlab,
data=NULL, subset=NULL,
sm=gs("smcont"), pooledvar=gs("pooledvar"),
method.smd=gs("method.smd"), sd.glass=gs("sd.glass"),
exact.smd=gs("exact.smd"),
level=gs("level"), level.comb=gs("level.comb"),
comb.fixed=gs("comb.fixed"), comb.random=gs("comb.random"),
hakn=gs("hakn"),
method.tau=gs("method.tau"), tau.preset=NULL, TE.tau=NULL,
tau.common=gs("tau.common"),
prediction=gs("prediction"), level.predict=gs("level.predict"),
method.bias=gs("method.bias"),
backtransf=gs("backtransf"),
title=gs("title"), complab=gs("complab"), outclab="",
label.e=gs("label.e"), label.c=gs("label.c"),
label.left=gs("label.left"), label.right=gs("label.right"),
byvar, bylab, print.byvar=gs("print.byvar"),
byseparator=gs("byseparator"),
keepdata=gs("keepdata"),
warn=gs("warn"))"DL", "PM", "REML", "ML", "HS",
"SJ", "HE", or "EB", can be abbreviated."rank", "linreg", or "mm", can
be abbreviated. See function metabiassm="ROM") should be back transformed in printouts
and plots. If TRUE (default), results will be presented as ratio
of means; otherwise log ratio of means will be shown."MD", "SMD", or "ROM") is to be used for
pooling of studies.sm="MD").sm="SMD"). Either "Hedges" for Hedges' g (default),
"Cohen" for Cohen's d, or "Glass" for Glass' delta,
can be abbreviated."control" using
the standard deviation in the control group (sd.c) or
"experimental" using the standard deviation in the
experimental group (sd.e), can be abbreviated.n.e).c("metacont", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:
"Inverse".hakn=TRUE).byvar is not
missing.byvar
is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing
and hakn=TRUE.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar
is not missing.byvar is not missing.byvar is not missing.byvar is not missing
(only calculated if argument tau.common is TRUE).byvar is not missing.byvar is not missing.byvar is not
missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is not missing.byvar is
not missing.byvar is not missing.byvar is
not missing.keepdata=TRUE).keepdata=TRUE).sm="MD")
sm="SMD")
sm="ROM")
method.smd="Hedges") - see Hedges (1981)
method.smd="Cohen") - see Cohen (1988)
method.smd="Glass") - see Glass (1976)
exact.smd=FALSE), an accurate approximation of this bias
provided in Hedges (1981) is utilised for Hedges' g as well as its
standard error; these approximations are also used in RevMan
5. Following Borenstein et al. (2009) these approximations are not
used in the estimation of Cohen's d. White and Thomas (2005) argued
that approximations are unnecessary with modern software and
accordingly promote to use the exact formulae; this is possible
using argument exact.smd=TRUE. For Hedges' g the exact
formulae are used to calculate the standardised mean difference as
well as the standard error; for Cohen's d the exact formula is only
used to calculate the standard error. In typical applications (with
sample sizes above 10), the differences between using the exact
formulae and the approximation will be minimal. For Glass' delta, by default (argument sd.glass="control"),
the standard deviation in the control group (sd.c) is used in
the denominator of the standard mean difference. The standard
deviation in the experimental group (sd.e) can be used by
specifying sd.glass="experimental". Calculations are conducted on the log scale for ratio of means
(sm="ROM"). Accordingly, list elements TE,
TE.fixed, and TE.random contain the logarithm of ratio
of means. In printouts and plots these values are back transformed
if argument backtransf=TRUE. For several arguments defaults settings are utilised (assignments
using gs function). These defaults can be changed
using the settings.meta function. Internally, both fixed effect and random effects models are
calculated regardless of values choosen for arguments
comb.fixed and comb.random. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random of an object of class "meta" even if
argument comb.random=FALSE. However, all functions in R
package meta will adequately consider the values for
comb.fixed and comb.random. E.g. function
print.meta will not print results for the random
effects model if comb.random=FALSE. The function metagen is called internally to calculate
individual and overall treatment estimates and standard errors. A prediction interval for treatment effect of a new study is
calculated (Higgins et al., 2009) if arguments prediction and
comb.random are TRUE. R function update.meta can be used to redo the
meta-analysis of an existing metacont object by only specifying
arguments which should be changed. For the random effects, the method by Hartung and Knapp (2003) is
used to adjust test statistics and confidence intervals if argument
hakn=TRUE. The DerSimonian-Laird estimate (1986) is used in the random effects
model if method.tau="DL". The iterative Paule-Mandel method
(1982) to estimate the between-study variance is used if argument
method.tau="PM". Internally, R function paulemandel is
called which is based on R function mpaule.default from R
package metRology from S.L.R. Ellison <s.ellison at
lgc.co.uk>. If R package metafor (Viechtbauer 2010) is installed, the
following methods to estimate the between-study variance
\(\tau^2\) (argument method.tau) are also available:
method.tau="REML")
method.tau="ML")
method.tau="HS")
method.tau="SJ")
method.tau="HE")
method.tau="EB").
rma.uni of R package
metafor is called internally. See help page of R function
rma.uni for more details on these methods to estimate
between-study variance.update.meta, metabin, metagendata(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
meta1
forest(meta1)
meta2 <- metacont(Fleiss93cont$n.e, Fleiss93cont$mean.e,
Fleiss93cont$sd.e,
Fleiss93cont$n.c, Fleiss93cont$mean.c,
Fleiss93cont$sd.c,
sm="SMD")
meta2
data(amlodipine)
meta3 <- metacont(n.amlo, mean.amlo, sqrt(var.amlo),
n.plac, mean.plac, sqrt(var.plac),
data=amlodipine, studlab=study)
summary(meta3)
# Use pooled variance
#
meta4 <- metacont(n.amlo, mean.amlo, sqrt(var.amlo),
n.plac, mean.plac, sqrt(var.plac),
data=amlodipine, studlab=study,
pooledvar=TRUE)
summary(meta4)
# Use Cohen's d instead of Hedges' g as effect measure
#
meta5 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont,
sm="SMD", method.smd="Cohen")
meta5
# Use Glass' delta instead of Hedges' g as effect measure
#
meta6 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont,
sm="SMD", method.smd="Glass")
meta6
# Use Glass' delta based on the standard deviation in the experimental group
#
meta7 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont,
sm="SMD", method.smd="Glass", sd.glass="experimental")
meta7
# Calculate Hedges' g based on exact formulae
#
update(meta1, exact.smd=TRUE)
#
# Meta-analysis of response ratios (Hedges et al., 1999)
#
data(woodyplants)
meta8 <- metacont(n.elev, mean.elev, sd.elev,
n.amb, mean.amb, sd.amb,
data=woodyplants, sm="ROM")
summary(meta8)
summary(meta8, backtransf=FALSE)
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